Relatively narrow sets of methods define eras like genomics and proteomics. The instruments used to practice these methods are often badly mismatched to the biological agenda. A bottleneck now exists as biology moves to the biological cell, and genomic and proteomic approaches have increased the encyclopedia of molecules and interactions to the point where one can practice broad combinatorial experiments in cells. The primary tools for read-out of these experiments include microscopy, cytometry, arrays, fluorimeters and a handful of biochemical assays.
Because it can quickly produce a statistically significant reading, one of the most important of these tools is the fluorescence-activated flow cytometer (FACS). However in several dimensions FACS is inadequate to the agenda. It is only practical to make measurements on a few variables (typically 2 or 3 variables) at a time and thus compromises sample throughput. In addition FACS does not provide valuable intracellular information. In contrast, high-content screening (HCS, i.e., Imaging cytometry or high-throughput automated microscopy) (Taylor, D. L., et al. Humana Press, Totowa N. J. 2007; Bullen A., Nature Reviews 7, 54-93, 2008; Haney, S. A., et al., Drug Discovery Today, 11, 889-894, 2008; Gough, A. H. and Johnston, P. A., Methods Mol. Biol. 356, 41-51, 2007; Lee, S., and Howel, B. J., Methods Enzymol. 414, 468-483, 2006; Pepperkok, R. and Ellenberg, J. Nat. Rev. Cell Biol. 7, 690-696, 2006; Eggert, U. S. and Mitchinson, T. J., Curr. Opin. Chem. Biol. 10, 232-237, 2006; Loo, L. H., et al., Nature Methods 4, 445-453, 2007; Proceedings of the Symposium on High-Content Analysis, San Fransisco, Calif., Jan. 5-9, 2009; Bleicher, K. H., et al., Nature Rev. 2, 369, 2003; Lang, P. et al., Nature Rev. 5, 343-356, 2006; Ding, G. J. F., et al. J. Bio. Chem. 273., 28897, 1998; George, T. C., et al., J Immuno. Methods 311, 117-129, 2006; Lindblad, J., et al., Cytometry A, 57, 22-23, 2004; Nuclear texture: Abramoff, M. D., Magalhaes, P. J., and Dam, S. J. Biophotonics Intern. 11, 36-42 2004; Carpenter A. E., et al. Genome Biology, 7:R100, 2006; Carpenter, A. E., et al., Nat. Rev. Genet. 5, 11-22, 2004; Wheeler, D. B., et al., Nature Genetics, 37, s25-s30, 2005) is an attempt to add more information to the content of FACS, but high content screening (HCS) methods struggle to achieve a useful assay speed (Eggert, U. S. and Mitchinson, T. J., Curr. Opin. Chem. Biol. 10, 232-237, 2006; Loo, L. H., et al., Nature Methods 4, 445-453, 2007; Proceedings of the Symposium on High-Content Analysis, San Fransisco, Calif., Jan. 5-9, 2009; Bleicher, K. H., et al., Nature Rev. 2, 369, 2003; Lang, P. et al., Nature Rev. 5, 343-356, 2006). Throughput of both FACS and HCS is an issue for readout of combinatorial biology in general, but particularly with live cells. For example, nuclear translocation kinetics, the basis of the most successful high content screening (HCS) assay, often have a half-time response of 5-10 minutes (Ding, G. J. F., et al. J. Bio. Chem. 273., 28897, 1998). In a live-cell kinetic study it is usually not possible to read a single 96-well HCS plate in this time. Furthermore, for either flow cytometry or HCS, fixing cells causes protein reorganization, and many cytokine modifiers can show alternatively agonism or antagonism in a dose-dependant fashion. Therefore, the biology of nuclear translocation calls out for dose-response curves taken over many dose levels, on live cells, and with time response on the order of several minutes. Except when a very limited number of compounds are to be tested, the first HCS methods remain orders of magnitude mismatched in speed for real needs of combinatorial drug discovery.
Furthermore the first HCS methods still do not monitor cells at a sufficient level of multi-parameter complexity. Even in the most controlled conditions biological samples are highly heterogeneous at the cellular level. There is a need for a whole new class of tools that are capable of gathering many more simultaneous parameters (specifically multiplex expression analysis) from well-defined subpopulations of cells. The multi-parameter complexity of early HCS is inadequate, particularly for studies with primary cells (e.g., cancer). Unfortunately HCS readout tools do not permit sorting by phenotype.
FACS has speed but is information poor and it does not multiplex well. HCS increases information content to a degree, but does not increase content sufficiently, is too slow, and does not separate (sort) cells, so it does not permit deeper post-cytometry analysis.
High-Content Screening in General: Several of the most common high-content assays implemented on microscopes (2-D) are (Taylor, D. L., et al. Humana Press, Totowa N. J. 2007; Bullen A., Nature Reviews 7, 54-93, 2008; Haney, S. A., et al., Drug Discovery Today, 11, 889-894, 2008; Gough, A. H. and Johnston, P. A., Methods Mol. Biol. 356, 41-51, 2007; Lee, S., and Howel, B. J., Methods Enzymol. 414, 468-483, 2006; Pepperkok, R. and Ellenberg, J. Nat. Rev. Cell Biol. 7, 690-696, 2006; Eggert, U. S. and Mitchinson, T. J., Curr. Opin. Chem. Biol. 10, 232-237, 2006; Loo, L. H., et al., Nature Methods 4, 445-453, 2007; Proceedings of the Symposium on High-Content Analysis, San Fransisco, Calif., Jan. 5-9, 2009; Bleicher, K. H., et al., Nature Rev. 2, 369, 2003; Lang, P. et al., Nature Rev. 5, 343-356, 2006; Ding, G. J. F., et al. J. Bio. Chem. 273., 28897, 1998; George, T. C., et al., J Immuno. Methods 311, 117-129, 2006; Lindblad, J., et al., Cytometry A, 57, 22-23, 2004; Nuclear texture: Abramoff, M. D., Magalhaes, P. J., and Dam, S. J. Biophotonics Intern. 11, 36-42 2004; Carpenter A. E., et al. Genome Biology, 7:R100, 2006; Carpenter, A. E., et al., Nat. Rev. Genet. 5, 11-22, 2004; Wheeler, D. B., et al., Nature Genetics, 37, s25-s30, 2005) (a) nuclear translocation (NT). The most common NT assay is NF-kB translocation. NF-kB is a transcription factor that is critical to cellular stress response. The p65 subunit is a sensitive to several known stimulants, for example, by altered interleukin ILa1 or tumor necrosis factor. The translocation to the nucleus is required to induce gene expression. (b) apoptosis. Image-based assays for apoptosis can provide more information than FACS. For example, by determining nucleus size, it is possible to ascertain necrotic or late apoptotic cells. The nucleus is stained and the image algorithm determines shape and size relative to the cell dimensions. (c) target activation. A very wide class of assays measure localization and total intensity from GFP fusions or other fluorescent markers. Cell cycle, receptor internalization, or drug resistance are commonly measured. (d) co-localization of markers. Co-localization is highly informative about biological mechanism. This is an enormous area of active research particularly in the field of biological development and imaging information is highly useful. (e) intracellular trafficking. Several microscope-based assays track the intracellular migration of molecules by programmed endocytosis. For example, Amnis Inc. has introduced an assay where the antibody CD20 is monitored and correlated with markers for endosomes and lysosomes. (f) morphology. The most obvious markers for phenotype are cell shape and area, however more subtle rearrangements of the cytoskeleton and location of organelles are also often used in microscope assays. (g) cell cycle. The progression of cell cycle is widely used in screening cancer therapies. The phase of individual cells is correlated with markers for specific proteins. Measurements are often also made on the dimensions or total DNA of the nucleus.
High-Content Screening Instruments: Several commercial 2-D HCS instruments are: (i)—CCD/automated microscopes (e.g., Thermo Scientific—Cellomics ArrayScan™, GE Healthcare—in Cell™ PerkinElmer—EvoTech Opera™, Molecular Devices IsoCyte™); (ii)—TDI CCD/flow cytometer (Amnis ImageStream™; (iii)—low-res laser scanners (CompuCyte iColor™, Acumen-Explorer™. These systems generally achieve assay rates of about 2-6 wells/min for real HCS assays. The Amnis ImageStream is unique as a CCD-based flow imaging system. However, it is generally slower than the microscope systems for most users, is a single-channel instrument, and has no sorting ability (nor could it sort efficiently without a faster sensor). The laser scanning instruments are not flow-based and generally do not resolve high-content information.
The normal workflow of biological assessment or screening a large number of biological cell samples is both time consuming, expensive on both reagents and biological cell samples and requires a series of separate analysis on different instruments. For example a common work flow in analysis of a large number of biological samples might contain in some or any order: (1) FACS (or CHip) to isolate cells of interest, (2) microscopy to identify localization changes using a marker, (3) hybridization array to qualitatively identify up-, down-regulation in response to a modifier and, (4) QPCR to get a quantitative measurement of expression changes (ideally at the level of about 100 genes, money and time allowing). One limiting issue is that a researcher needs to proceed serially through these methods on separate instruments with a great penalty in time and while trying to maintain a consistent sample for comparison purposes.
Several limitations exist for use of cell cytometry in a high through-put capacity. For instance, there is not an adequate cost-effective solution for (i) scale-up of HCS for drug discovery, (ii) for handling of small samples of highly heterogeneous primary cell types (e.g., flow biopsy or stem-cell biology); (iii) for finding rare cells (e.g. finding pluripotent cells for cancer diagnostics), (iv) image-based sorting/enrichment.
There also exists limitations for the uses of FACs, Automated Microscopy, and CCD Cytometers for high-throughput sorting based on phenotypic characteristics. For example, HCS with CCDs is frequently done in open wells (see e.g., Taylor, D. L., et al. Humana Press, Totowa N. J. 2007; Bullen A., Nature Reviews 7, 54-93, 2008; Haney, S. A., et al., Drug Discovery Today, 11, 889-894, 2008; Gough, A. H. and Johnston, P. A., Methods Mol. Biol. 356, 41-51, 2007; Lee, S., and Howel, B. J., Methods Enzymol. 414, 468-483, 2006; Pepperkok, R. and Ellenberg, J. Nat. Rev. Cell Biol. 7, 690-696, 2006; Eggert, U. S. and Mitchinson, T. J., Curr. Opin. Chem. Biol. 10, 232-237, 2006; Loo, L. H., et al., Nature Methods 4, 445-453, 2007; Proceedings of the Symposium on High-Content Analysis, San Fransisco, Calif., Jan. 5-9, 2009; Bleicher, K. H., et al., Nature Rev. 2, 369, 2003; Lang, P. et al., Nature Rev. 5, 343-356, 2006; Ding, G. J. F., et al. J. Bio. Chem. 273, 28897, 1998, Amnis website on the world wide web at amnis.com), on spotted slides (Carpenter A. E., et al. Genome Biology, 7:R100, 2006; Carpenter, A. E., et al., Nat. Rev. Genet. 5, 11-22, 2004; Wheeler, D. B., et al., Nature Genetics, 37, s25-s30, 2005), or in flow (George, T. C., et al J Immuno. Methods 311, 117-129, 2006). Even on high-density slides, the state of the art is largely determined by the performance of low-signal scientific CCD cameras. At 1024×1024-pixel image size, the frame rate due to buffering restrictions is either 15 or (conditionally) 30 frames a second (e.g., the latest Princeton Instruments/Roper or Hamamatsu). However even much slower rates are often mandated by low signal. Analysis of a single high-density spotted slide can take many hours. Autofocusing and mechanical motions, further limit throughput (accounting for the majority of the time budget on wide-field imaging systems. CCD-based Amnis imaging flow cytometers are more limited in throughput and users typically report raw data acquisition (unclassified cells) from such a machine at 100 objects/min (see e.g., information on the world wide web at amnis.com/applications.asp). Thus, high content microscope-based systems are too slow and do not have a sorting capacity. Additionally, data storage rapidly requires gigabytes of storage capacity.
Accordingly, there is a need for a single, automated instrument for high throughput cell sorting based on phenotypic distinction from only one or a few cells per reading (to reduce noise from cell heterogeneity) which is inexpensive to run and operate, and with sufficient throughput to generate statistically significant answers.